RTX 4060 Ti 16GB vs RTX 4070 12GB for Local LLMs
The RTX 4060 Ti 16GB holds bigger models (16.0 GB vs 12.0 GB), and the RTX 4070 12GB generates tokens about 74% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 4060 Ti 16GB | RTX 4070 12GB | |
|---|---|---|
| Memory | 16 GB | 12 GB |
| GPU-usable memory | 16.0 GB | 12.0 GB |
| Bandwidth | 288 GB/s | 504 GB/s |
| Architecture | Ada Lovelace | Ada Lovelace |
| Released | 2023-07 | 2023-04 |
| Llama 3.1 8B Q4_K_M | ~34 tok/s | ~59 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 4060 Ti 16GB | RTX 4070 12GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~34 tok/s | Fits ~59 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~19 tok/s | Fits ~34 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~64 tok/s | 81% on GPU ~51 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | 80% on GPU ~38 tok/s | 60% on GPU ~31 tok/s |
| Qwen 3 32B | 21.4 GB | 70% on GPU ~4.4 tok/s | 53% on GPU ~3.7 tok/s |
| Llama 3.3 70B | 43.2 GB | 35% on GPU ~1.3 tok/s | Too small |
| gpt-oss-120b | 62.0 GB | Too small | Too small |
Verdict
A classic trade-off: the 4070 has 75% more bandwidth (504 vs 288 GB/s), so it is much faster on models that fit in 12 GB. The 4060 Ti 16GB fits more, including gpt-oss-20b, Gemma 4 26B-A4B and 24B models at Q4_K_M. For LLMs, pick the 16 GB card if you want bigger models and the 4070 if you mostly run 8–14B models and also game.
Which should you buy?
Choose the RTX 4060 Ti 16GB if…
- You want to run gpt-oss-20b or 24B models.
- You need long contexts on 14B models.
- You want lower power use.
Choose the RTX 4070 12GB if…
- You mainly run 8–14B models and want speed.
- You also game at 1440p.
- You generate images at 1024×1024 with GGUF weights.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | RTX 4060 Ti 16GB: best quant | RTX 4070 12GB: best quant | RTX 4060 Ti 16GB: max context | RTX 4070 12GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | Q8_0 | Q8_0 | 256K | 164K |
| Qwen 3 14B | Q6_K | Q4_K_M | 37K | 13K |
| gpt-oss-20b | MXFP4 (official) | Q3_K_M | 55K | – |
| Qwen 3.6 27B | IQ4_XS | Q2_K | – | – |
| Qwen 3 32B | Q2_K | Does not fit | – | – |
| Llama 3.3 70B | Does not fit | Does not fit | – | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | RTX 4060 Ti 16GB | RTX 4070 12GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP8 | GGUF Q6_K |
| Qwen-Image | GGUF Q5_K_S | Needs offloading |
| Wan 2.2 A14B (T2V / I2V) | GGUF Q5_K_S | Needs offloading |
Models only one card can run
Only RTX 4060 Ti 16GB
Only RTX 4070 12GB
None
Frequently asked questions
Is the RTX 4060 Ti 16GB or RTX 4070 12GB better for local LLMs?
A classic trade-off: the 4070 has 75% more bandwidth (504 vs 288 GB/s), so it is much faster on models that fit in 12 GB. The 4060 Ti 16GB fits more, including gpt-oss-20b, Gemma 4 26B-A4B and 24B models at Q4_K_M. For LLMs, pick the 16 GB card if you want bigger models and the 4070 if you mostly run 8–14B models and also game.
Which is faster for LLM inference?
The RTX 4070 12GB, by about 74% for token generation on models that fit both (about 59 vs 34 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The RTX 4060 Ti 16GB, with 16.0 GB usable versus 12.0 GB.